This renewal application will combine statistical analyses of extraordinary datasets with mathematical models and optimization techniques to design epidemic control strategies in both resource-rich and resource- constrained countries. In addition, strategies for optimizing the expansion of treatment programs will be designed. The recent development of antiretroviral-based tools to prevent HIV transmission could potentially alter the course of the HIV pandemic. At the same time, the rollout of ARV treatment in Africa is expanding dramatically. Uncertainty about the number of individuals who are undiagnosed and infected with HIV (i.e., the size of the """"""""hidden epidemic"""""""") is a critical obstacle to the success o both treatment programs and interventions. We will estimate the size of the """"""""hidden epidemic"""""""" in Denmark (in Copenhagen), Botswana, and Lesotho, through analyses of data provided by collaborators in these countries. These estimates will form the foundation for our analyses.
In Aim 1 A we evaluate, for the Danish community of men who have sex with men, whether increasing levels of treatment have been correlated with decreasing HIV incidence;
in Aim 1 B we use modeling to determine the conditions that need to be met for """"""""treatment as prevention""""""""-based interventions to eliminate HIV in that community.
In Aim 2 we use georeferenced data to construct country-level predictive HIV prevalence maps for, and estimate the burden of disease in, Botswana and Lesotho;two countries with among the most severe HIV epidemics worldwide. We use these maps to quantify the accessibility to treatment across each country and to determine how to optimize the efficiency of treatment programs (e.g., minimize interruptions in the treatment supply chain).
In Aim 3, using the results from Aim 2, we construct transmission models and determine how to optimize the design of interventions to reduce HIV transmission;specifically to determine how best to minimize incidence, under resource constraints.

Public Health Relevance

This grant will use mathematical models and statistical analyses to predict the impact, in both resource-rich and resource-constrained countries, of newly available interventions designed to prevent HIV transmission. It will also use these methods to design optimal strategies, in resource-constrained countries for both expanding anti-HIV treatment and for rolling out interventions.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
High Priority, Short Term Project Award (R56)
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AIDS Clinical Studies and Epidemiology Study Section (ACE)
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Mckaig, Rosemary G
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University of California Los Angeles
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Los Angeles
United States
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Coburn, Brian J; Okano, Justin T; Blower, Sally (2017) Using geospatial mapping to design HIV elimination strategies for sub-Saharan Africa. Sci Transl Med 9:
Okano, Justin T; Gerstoft, Jan; Obel, Niels et al. (2016) HIV elimination and population viral load. Lancet HIV 3:e507-e509
Okano, Justin T; Robbins, Danielle; Palk, Laurence et al. (2016) Testing the hypothesis that treatment can eliminate HIV: a nationwide, population-based study of the Danish HIV epidemic in men who have sex with men. Lancet Infect Dis 16:789-796
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Posta, Filippo; D'Orsogna, Maria R; Chou, Tom (2009) Enhancement of cargo processivity by cooperating molecular motors. Phys Chem Chem Phys 11:4851-60